/**

* @license

* Copyright 2018 Google LLC. All Rights Reserved.

* Licensed under the Apache License, Version 2.0 (the "License");

* you may not use this file except in compliance with the License.

* You may obtain a copy of the License at

*

* http://www.apache.org/licenses/LICENSE-2.0

*

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

* =============================================================================

*/

import {ENGINE} from '../engine';

import {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '../kernel_names';

import {NamedAttrMap} from '../kernel_registry';

import {Tensor, Tensor4D} from '../tensor';

import {NamedTensorMap} from '../tensor_types';

import {convertToTensor} from '../tensor_util_env';

import {TensorLike} from '../types';

import {op} from './operation';

/**

* Computes the 2D max pooling of an image with Argmax index.

* The indices in argmax are flattened, so that a maximum value at position `[b,

* y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if

* include_batch_in_index is False; `((b * height + y) * width + x) * channels

* +c` if include_batch_in_index is True.

*

* The indices returned are always in `[0, height) x [0, width)` before

* flattening.

*

* @param x The input tensor, of rank 4 or rank 3 of shape

* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.

* @param filterSize The filter size: `[filterHeight, filterWidth]`. If

* `filterSize` is a single number, then `filterHeight == filterWidth`.

* @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If

* `strides` is a single number, then `strideHeight == strideWidth`.

* @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to

* "NDHWC". Specify the data format of the input and output data. With the

* default format "NDHWC", the data is stored in the order of: [batch,

* depth, height, width, channels]. Only "NDHWC" is currently supported.

* @param pad The type of padding algorithm.

* - `same` and stride 1: output will be of same size as input,

* regardless of filter size.

* - `valid`: output will be smaller than input if filter is larger

* than 1x1.

* - For more info, see this guide:

* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](

* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)

* @param includeBatchIndex Defaults to False. Whether to include batch

* dimension in flattened index of argmax.

*

* @doc {heading: 'Operations', subheading: 'Convolution'}

*/

function maxPoolWithArgmax_<T extends Tensor4D>(

}

export const maxPoolWithArgmax = /* @__PURE__ */ op({maxPoolWithArgmax_});